Present work considers the minimization of the bi-criteria function including weighted sum of makespan and total completion time for a Multiprocessor task scheduling problem.Genetic algorithm is the most
appealing choice for the different NP hard problems including multiprocessor task scheduling.
Performance of genetic algorithm depends on the quality of initial solution as good initial solution provides the better results. Different list scheduling heuristics based hybrid genetic algorithms (HGAs) have been
proposed and developedfor the problem. Computational analysis with the help of defined performance
index has been conducted on the standard task scheduling problems for evaluating the performance of the
proposed HGAs. The analysis shows that the ETF-GA is quite efficient and best among the other heuristic based hybrid genetic algorithms in terms of solution quality especially for large and complex problems.
An Improved Parallel Activity scheduling algorithm for large datasetsIJERA Editor
Parallel processing is capable of executing a large number of tasks on a multiprocessor at the same time period, and it is also one of the emerging concepts. Complex and computational problems can be resolved in an efficient way with the help of parallel processing. The parallel processing system can be divided into two categories depending on the nature of tasks such are homogenous parallel system and the heterogeneous parallel processing system. In the homogeneous environment, the number of processors required for executing different tasks is similar in capacity. In case of heterogeneous environments, tasks are allocated to various processors with different capacity and speed. The main objective of parallel processing is to optimize the execution speed and to shorten the duration of task execution with independent of environment. In this proposed work, an optimized parallel project selection method was implemented to find the optimal resource utilization and project scheduling. The execution speeds of the task increases and the overall average execution time of the task decreases by allocating different tasks to various processors with the task scheduling algorithm.
Manager’s Preferences Modeling within Multi-Criteria Flowshop Scheduling Prob...Waqas Tariq
This paper proposes a metaheuristic to solve the permutation flow shop scheduling problem where several criteria are to be considered, such as: the makespan, total flowtime and total tardiness of jobs. The proposed metaheuristic is based on tabu search algorithm. The Compromise Programming model and the concept of satisfaction functions are utilized to integrate explicitly the Manager’s preferences. The proposed approach has been tested through a computational experiment. This approach can be useful for large scale scheduling problems and the Manager can consider additional scheduling criteria.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
Grid computing is a hardware and software infrastructure and provides affordable, sustainable, and reliable access. Its aim is
to create a supercomputer using free resources. One of the challenges to the Grid computing is scheduling problem which is regarded
as a tough issue. Since scheduling problem is a non-deterministic issue in the Grid, deterministic algorithms cannot be used to improve
scheduling.
In this paper, a combination of genetic algorithms and binary gravitational attraction is used for scheduling problem solving, where the
reduction in the duty performance timing and cost-effective use of simultaneous resources are investigated. In this case, the user
determines the execution time parameter and cost-effective use of resources. In this algorithm, a new approach that has led to a
balanced load of resources is used in the selection of resources. Experimental results reveals that our proposed algorithm in terms of
cost-time and selection of the best resource has reached better results than other algorithm.
The task scheduling is a key process in large-scale distributed systems like cloud computing infrastructures
which can have much impressed on system performance. This problem is referred to as a NP-hard problem
because of some reasons such as heterogeneous and dynamic features and dependencies among the
requests. Here, we proposed a bi-objective method called DWSGA to obtain a proper solution for
allocating the requests on resources. The purpose of this algorithm is to earn the response quickly, with
some goal-oriented operations. At first, it makes a good initial population by a special way that uses a bidirectional
tasks prioritization. Then the algorithm moves to get the most appropriate possible solution in a
conscious manner by focus on optimizing the makespan, and considering a good distribution of workload
on resources by using efficient parameters in the mentioned systems. Here, the experiments indicate that
the DWSGA amends the results when the numbers of tasks are increased in application graph, in order to
mentioned objectives. The results are compared with other studied algorithms.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
An Improved Parallel Activity scheduling algorithm for large datasetsIJERA Editor
Parallel processing is capable of executing a large number of tasks on a multiprocessor at the same time period, and it is also one of the emerging concepts. Complex and computational problems can be resolved in an efficient way with the help of parallel processing. The parallel processing system can be divided into two categories depending on the nature of tasks such are homogenous parallel system and the heterogeneous parallel processing system. In the homogeneous environment, the number of processors required for executing different tasks is similar in capacity. In case of heterogeneous environments, tasks are allocated to various processors with different capacity and speed. The main objective of parallel processing is to optimize the execution speed and to shorten the duration of task execution with independent of environment. In this proposed work, an optimized parallel project selection method was implemented to find the optimal resource utilization and project scheduling. The execution speeds of the task increases and the overall average execution time of the task decreases by allocating different tasks to various processors with the task scheduling algorithm.
Manager’s Preferences Modeling within Multi-Criteria Flowshop Scheduling Prob...Waqas Tariq
This paper proposes a metaheuristic to solve the permutation flow shop scheduling problem where several criteria are to be considered, such as: the makespan, total flowtime and total tardiness of jobs. The proposed metaheuristic is based on tabu search algorithm. The Compromise Programming model and the concept of satisfaction functions are utilized to integrate explicitly the Manager’s preferences. The proposed approach has been tested through a computational experiment. This approach can be useful for large scale scheduling problems and the Manager can consider additional scheduling criteria.
Proposing a scheduling algorithm to balance the time and cost using a genetic...Editor IJCATR
Grid computing is a hardware and software infrastructure and provides affordable, sustainable, and reliable access. Its aim is
to create a supercomputer using free resources. One of the challenges to the Grid computing is scheduling problem which is regarded
as a tough issue. Since scheduling problem is a non-deterministic issue in the Grid, deterministic algorithms cannot be used to improve
scheduling.
In this paper, a combination of genetic algorithms and binary gravitational attraction is used for scheduling problem solving, where the
reduction in the duty performance timing and cost-effective use of simultaneous resources are investigated. In this case, the user
determines the execution time parameter and cost-effective use of resources. In this algorithm, a new approach that has led to a
balanced load of resources is used in the selection of resources. Experimental results reveals that our proposed algorithm in terms of
cost-time and selection of the best resource has reached better results than other algorithm.
The task scheduling is a key process in large-scale distributed systems like cloud computing infrastructures
which can have much impressed on system performance. This problem is referred to as a NP-hard problem
because of some reasons such as heterogeneous and dynamic features and dependencies among the
requests. Here, we proposed a bi-objective method called DWSGA to obtain a proper solution for
allocating the requests on resources. The purpose of this algorithm is to earn the response quickly, with
some goal-oriented operations. At first, it makes a good initial population by a special way that uses a bidirectional
tasks prioritization. Then the algorithm moves to get the most appropriate possible solution in a
conscious manner by focus on optimizing the makespan, and considering a good distribution of workload
on resources by using efficient parameters in the mentioned systems. Here, the experiments indicate that
the DWSGA amends the results when the numbers of tasks are increased in application graph, in order to
mentioned objectives. The results are compared with other studied algorithms.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
Natural convection in a differentially heated cavity plays a
major role in the understanding of flow physics and heat
transfer aspects of various applications. Parameters such as
Rayleigh number, Prandtl number, aspect ratio, inclination
angle and surface emissivity are considered to have either
individual or grouped effect on natural convection in an
enclosed cavity. In spite of this, simultaneous study of these
parameters over a wide range is rare. Development of
correlation which helps to investigate the effect of the large
number and wide range of parameters is challenging. The
number of simulations required to generate correlations for
even a small number of parameters is extremely large. Till
date there is no streamlined procedure to optimize the number
of simulations required for correlation development.
Therefore, the present study aims to optimize the number of
simulations by using Taguchi technique and later generate
correlations by employing multiple variable regression
analysis. It is observed that for a wide range of parameters,
the proposed CFD-Taguchi-Regression approach drastically
reduces the total number of simulations for correlation
generation.
ROBUST OPTIMIZATION FOR RCPSP UNDER UNCERTAINTYijseajournal
The aim of the present article is to optimize the robustness objective for the Resource-Constrained Project
scheduling Problem (RCPSP) dealing with activity duration uncertainty. The studied robustness consists in
minimizing the worst-case performance, referred to as the min-max robustness objective, among a set of
initial scenarios. We propose an enhanced GRASP approach as a solution to the given scenario-based
robust model. This approach is based on different priority rules in the construction phase and a forwardbackward
heuristic in the improvement phase. We investigate two different benchmark data sets, the
Patterson set and the PSPLIB J30 set. Experiments show that the proposed enhanced GRASP outperforms
the basic procedure, and a based-evolutionary algorithm, in robustness optimization.
A Novel Approach to Mathematical Concepts in Data Miningijdmtaiir
-This paper describes three different fundamental
mathematical programming approaches that are relevant to
data mining. They are: Feature Selection, Clustering and
Robust Representation. This paper comprises of two clustering
algorithms such as K-mean algorithm and K-median
algorithms. Clustering is illustrated by the unsupervised
learning of patterns and clusters that may exist in a given
databases and useful tool for Knowledge Discovery in
Database (KDD). The results of k-median algorithm are used
to collecting the blood cancer patient from a medical database.
K-mean clustering is a data mining/machine learning algorithm
used to cluster observations into groups of related observations
without any prior knowledge of those relationships. The kmean algorithm is one of the simplest clustering techniques
and it is commonly used in medical imaging, biometrics and
related fields.
AN INTEGER-LINEAR ALGORITHM FOR OPTIMIZING ENERGY EFFICIENCY IN DATA CENTERSijfcstjournal
Nowadays, to meet the enormous computational requests, energy consumption, the largest part which is
related to idle resources, is strictly increased as a great part of a data center's budget. So, minimizing
energy consumption is one of the most important issues in the field of green computing. In this paper, we
present a mathematical model formed as integer-linear programming which minimizes energy consumption
and maximizes user’s satisfaction, simultaneously. However, migration variables, as principal decision
variables of the model, can be relaxed to continuous activities in some practical problems. This constraint
relaxation helps a decision maker to find faster solutions that are usually good approximations for
optimum. Near feasible solutions (infeasible solutions that are desirably close to the feasible region) have
been investigated as another relaxation considering the kind of solutions. For this purpose, we initially
present a measure to evaluate the amount of infeasibility of solutions and then let the model consider an
extended region including solutions with remissible infeasibility, if necessary.
Load balancing functionalities are crucial for best Grid performance and utilization. Accordingly,this paper presents a new meta-scheduling method called TunSys. It is inspired from the natural phenomenon of heat propagation and thermal equilibrium. TunSys is based on a Grid polyhedron model with a spherical like structure used to ensure load balancing through a local neighborhood propagation strategy. Furthermore, experimental results compared to FCFS, DGA and HGA show encouraging results in terms of system performance and scalability and in terms of load balancing efficiency.
Hybrid Multi-Gradient Explorer Algorithm for Global Multi-Objective OptimizationeArtius, Inc.
Hybrid Multi-Gradient Explorer (HMGE) algorithm for global multi-objective
optimization of objective functions considered in a multi-dimensional domain is presented. The proposed hybrid algorithm relies on genetic variation operators for creating new solutions, but in addition to a standard random mutation operator, HMGE
uses a gradient mutation operator, which improves convergence. Thus, random mutation helps find global Pareto frontier, and gradient mutation improves convergence to the
Pareto frontier. In such a way HMGE algorithm combines advantages of both
gradient-based and GA-based optimization techniques: it is as fast as a pure gradient-based MGE algorithm, and is able to find the global Pareto frontier similar to genetic algorithms
(GA). HMGE employs Dynamically Dimensioned Response Surface Method (DDRSM) for calculating gradients. DDRSM dynamically recognizes the most significant design variables, and builds local approximations based only on the variables. This allows one to
estimate gradients by the price of 4-5 model evaluations without significant loss of accuracy. As a result, HMGE efficiently optimizes highly non-linear models with dozens and hundreds of design variables, and with multiple Pareto fronts. HMGE efficiency is 2-10
times higher when compared to the most advanced commercial GAs.
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
Grasp approach to rcpsp with min max robustness objectivecsandit
This paper deals with the Resource-Constrained Project scheduling Problem (RCPSP) under
activity duration uncertainty. Based on scenarios, the object is to minimize the worst-case
performance among a set of initial scenarios which is referred to as the min-max robustness
objective. Due to the complexity of the tackled problem, we propose the application of the
GRASP method which is qualified as a simple and effective multi-start metaheuristic. The
proposed approach incorporates an adaptive greedy function based on priority rules to
construct new solutions, and a local search with a forward-backward heuristic in the
improvement phase. Two different benchmark data sets are investigated, the Patterson set and
the PSPLIB J30 set. Comparative results show that the proposed enhanced GRASP outperforms
the basic procedure in robustness optimization.
Threshold benchmarking for feature ranking techniquesjournalBEEI
In prediction modeling, the choice of features chosen from the original feature set is crucial for accuracy and model interpretability. Feature ranking techniques rank the features by its importance but there is no consensus on the number of features to be cut-off. Thus, it becomes important to identify a threshold value or range, so as to remove the redundant features. In this work, an empirical study is conducted for identification of the threshold benchmark for feature ranking algorithms. Experiments are conducted on Apache Click dataset with six popularly used ranker techniques and six machine learning techniques, to deduce a relationship between the total number of input features (N) to the threshold range. The area under the curve analysis shows that ≃ 33-50% of the features are necessary and sufficient to yield a reasonable performance measure, with a variance of 2%, in defect prediction models. Further, we also find that the log2(N) as the ranker threshold value represents the lower limit of the range.
Software size estimation at early stages of project development holds great significance to meet the competitive demands of software industry. Software size represents one of the most
interesting internal attributes which has been used in several effort/cost models as a predictor of effort and cost needed to design and implement the software. The whole world is focusing
towards object oriented paradigm thus it is essential to use an accurate methodology for measuring the size of object oriented projects. The class point approach is used to quantify classes which are the logical building blocks in object oriented paradigm. In this paper, we propose a class point based approach for software size estimation of On-Line Analytical
Processing (OLAP) systems. OLAP is an approach to swiftly answer decision support queries based on multidimensional view of data. Materialized views can significantly reduce the
execution time for decision support queries. We perform a case study based on the TPC-H benchmark which is a representative of OLAP System. We have used a Greedy based approach
to determine a good set of views to be materialized. After finding the number of views, the class point approach is used to estimate the size of an OLAP System The results of our approach are validated.
Natural convection in a differentially heated cavity plays a
major role in the understanding of flow physics and heat
transfer aspects of various applications. Parameters such as
Rayleigh number, Prandtl number, aspect ratio, inclination
angle and surface emissivity are considered to have either
individual or grouped effect on natural convection in an
enclosed cavity. In spite of this, simultaneous study of these
parameters over a wide range is rare. Development of
correlation which helps to investigate the effect of the large
number and wide range of parameters is challenging. The
number of simulations required to generate correlations for
even a small number of parameters is extremely large. Till
date there is no streamlined procedure to optimize the number
of simulations required for correlation development.
Therefore, the present study aims to optimize the number of
simulations by using Taguchi technique and later generate
correlations by employing multiple variable regression
analysis. It is observed that for a wide range of parameters,
the proposed CFD-Taguchi-Regression approach drastically
reduces the total number of simulations for correlation
generation.
ROBUST OPTIMIZATION FOR RCPSP UNDER UNCERTAINTYijseajournal
The aim of the present article is to optimize the robustness objective for the Resource-Constrained Project
scheduling Problem (RCPSP) dealing with activity duration uncertainty. The studied robustness consists in
minimizing the worst-case performance, referred to as the min-max robustness objective, among a set of
initial scenarios. We propose an enhanced GRASP approach as a solution to the given scenario-based
robust model. This approach is based on different priority rules in the construction phase and a forwardbackward
heuristic in the improvement phase. We investigate two different benchmark data sets, the
Patterson set and the PSPLIB J30 set. Experiments show that the proposed enhanced GRASP outperforms
the basic procedure, and a based-evolutionary algorithm, in robustness optimization.
A Novel Approach to Mathematical Concepts in Data Miningijdmtaiir
-This paper describes three different fundamental
mathematical programming approaches that are relevant to
data mining. They are: Feature Selection, Clustering and
Robust Representation. This paper comprises of two clustering
algorithms such as K-mean algorithm and K-median
algorithms. Clustering is illustrated by the unsupervised
learning of patterns and clusters that may exist in a given
databases and useful tool for Knowledge Discovery in
Database (KDD). The results of k-median algorithm are used
to collecting the blood cancer patient from a medical database.
K-mean clustering is a data mining/machine learning algorithm
used to cluster observations into groups of related observations
without any prior knowledge of those relationships. The kmean algorithm is one of the simplest clustering techniques
and it is commonly used in medical imaging, biometrics and
related fields.
AN INTEGER-LINEAR ALGORITHM FOR OPTIMIZING ENERGY EFFICIENCY IN DATA CENTERSijfcstjournal
Nowadays, to meet the enormous computational requests, energy consumption, the largest part which is
related to idle resources, is strictly increased as a great part of a data center's budget. So, minimizing
energy consumption is one of the most important issues in the field of green computing. In this paper, we
present a mathematical model formed as integer-linear programming which minimizes energy consumption
and maximizes user’s satisfaction, simultaneously. However, migration variables, as principal decision
variables of the model, can be relaxed to continuous activities in some practical problems. This constraint
relaxation helps a decision maker to find faster solutions that are usually good approximations for
optimum. Near feasible solutions (infeasible solutions that are desirably close to the feasible region) have
been investigated as another relaxation considering the kind of solutions. For this purpose, we initially
present a measure to evaluate the amount of infeasibility of solutions and then let the model consider an
extended region including solutions with remissible infeasibility, if necessary.
Load balancing functionalities are crucial for best Grid performance and utilization. Accordingly,this paper presents a new meta-scheduling method called TunSys. It is inspired from the natural phenomenon of heat propagation and thermal equilibrium. TunSys is based on a Grid polyhedron model with a spherical like structure used to ensure load balancing through a local neighborhood propagation strategy. Furthermore, experimental results compared to FCFS, DGA and HGA show encouraging results in terms of system performance and scalability and in terms of load balancing efficiency.
Hybrid Multi-Gradient Explorer Algorithm for Global Multi-Objective OptimizationeArtius, Inc.
Hybrid Multi-Gradient Explorer (HMGE) algorithm for global multi-objective
optimization of objective functions considered in a multi-dimensional domain is presented. The proposed hybrid algorithm relies on genetic variation operators for creating new solutions, but in addition to a standard random mutation operator, HMGE
uses a gradient mutation operator, which improves convergence. Thus, random mutation helps find global Pareto frontier, and gradient mutation improves convergence to the
Pareto frontier. In such a way HMGE algorithm combines advantages of both
gradient-based and GA-based optimization techniques: it is as fast as a pure gradient-based MGE algorithm, and is able to find the global Pareto frontier similar to genetic algorithms
(GA). HMGE employs Dynamically Dimensioned Response Surface Method (DDRSM) for calculating gradients. DDRSM dynamically recognizes the most significant design variables, and builds local approximations based only on the variables. This allows one to
estimate gradients by the price of 4-5 model evaluations without significant loss of accuracy. As a result, HMGE efficiently optimizes highly non-linear models with dozens and hundreds of design variables, and with multiple Pareto fronts. HMGE efficiency is 2-10
times higher when compared to the most advanced commercial GAs.
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
Grasp approach to rcpsp with min max robustness objectivecsandit
This paper deals with the Resource-Constrained Project scheduling Problem (RCPSP) under
activity duration uncertainty. Based on scenarios, the object is to minimize the worst-case
performance among a set of initial scenarios which is referred to as the min-max robustness
objective. Due to the complexity of the tackled problem, we propose the application of the
GRASP method which is qualified as a simple and effective multi-start metaheuristic. The
proposed approach incorporates an adaptive greedy function based on priority rules to
construct new solutions, and a local search with a forward-backward heuristic in the
improvement phase. Two different benchmark data sets are investigated, the Patterson set and
the PSPLIB J30 set. Comparative results show that the proposed enhanced GRASP outperforms
the basic procedure in robustness optimization.
Threshold benchmarking for feature ranking techniquesjournalBEEI
In prediction modeling, the choice of features chosen from the original feature set is crucial for accuracy and model interpretability. Feature ranking techniques rank the features by its importance but there is no consensus on the number of features to be cut-off. Thus, it becomes important to identify a threshold value or range, so as to remove the redundant features. In this work, an empirical study is conducted for identification of the threshold benchmark for feature ranking algorithms. Experiments are conducted on Apache Click dataset with six popularly used ranker techniques and six machine learning techniques, to deduce a relationship between the total number of input features (N) to the threshold range. The area under the curve analysis shows that ≃ 33-50% of the features are necessary and sufficient to yield a reasonable performance measure, with a variance of 2%, in defect prediction models. Further, we also find that the log2(N) as the ranker threshold value represents the lower limit of the range.
Software size estimation at early stages of project development holds great significance to meet the competitive demands of software industry. Software size represents one of the most
interesting internal attributes which has been used in several effort/cost models as a predictor of effort and cost needed to design and implement the software. The whole world is focusing
towards object oriented paradigm thus it is essential to use an accurate methodology for measuring the size of object oriented projects. The class point approach is used to quantify classes which are the logical building blocks in object oriented paradigm. In this paper, we propose a class point based approach for software size estimation of On-Line Analytical
Processing (OLAP) systems. OLAP is an approach to swiftly answer decision support queries based on multidimensional view of data. Materialized views can significantly reduce the
execution time for decision support queries. We perform a case study based on the TPC-H benchmark which is a representative of OLAP System. We have used a Greedy based approach
to determine a good set of views to be materialized. After finding the number of views, the class point approach is used to estimate the size of an OLAP System The results of our approach are validated.
Esta breve presentación nos muestra los tips que debemos tener en cuenta a la hora de planificar una huerta en el balcón y el jardín y lograr resultados exitosos.
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DGBSA : A BATCH JOB SCHEDULINGALGORITHM WITH GA WITH REGARD TO THE THRESHOLD ...IJCSEA Journal
In this paper , we will provide a scheduler on batch jobs with GA regard to the threshold detector. In The algorithm proposed in this paper, we will provide the batch independent jobs with a new technique ,so we can optimize the schedule them. To do this, we use a threshold detector then among the selected jobs, processing resources can process batch jobs with priority. Also hierarchy of tasks in each batch, will be determined with using DGBSA algorithm. Now , with the regard to the works done by previous ,we can provide an algorithm that by adding specific parameters to fitness function of the previous algorithms ,develop a optimum fitness function that in the proposed algorithm has been used. According to assessment done on DGBSA algorithm, in compare with the similar algorithms, it has more performance. The effective parameters that used in the proposed algorithm can reduce the total wasting time in compare with previous algorithms. Also this algorithm can improve the previous problems in batch processing with a new technique.
A novel scheduling algorithm for cloud computing environmentSouvik Pal
Cloud computing is the most recent computing paradigm, in the
Information Technology where the resources and information are provided
on-demand and accessed over the Internet. An essential factor in the cloud computing
system is Task Scheduling that relates to the efficiency of the entire cloud
computing environment. Mostly in a cloud environment, the issue of scheduling is
to apportion the tasks of the requesting users to the available resources. This paper
aims to offer a genetic based scheduling algorithm that reduces the waiting time of
the overall system. However the tasks enter the cloud environment and the users
have to wait until the resources are available that leads to more queue length and
increased waiting time. This paper introduces a Task Scheduling algorithm based
on genetic algorithm using a queuing model to minimize the waiting time and
queue length of the system.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
Grid computing enlarge with computing platform which is collection of heterogeneous computing resources connected by a network across dynamic and geographically dispersed organization to form a distributed high performance computing infrastructure. Grid computing solves the complex computing
problems amongst multiple machines. Grid computing solves the large scale computational demands in a high performance computing environment. The main emphasis in the grid computing is given to the resource management and the job scheduler .The goal of the job scheduler is to maximize the resource utilization and minimize the processing time of the jobs. Existing approaches of Grid scheduling doesn’t give much emphasis on the performance of a Grid scheduler in processing time parameter. Schedulers allocate resources to the jobs to be executed using the First come First serve algorithm. In this paper, we have provided an optimize algorithm to queue of the scheduler using various scheduling methods like Shortest Job First, First in First out, Round robin. The job scheduling system is responsible to select best suitable machines in a grid for user jobs. The management and scheduling system generates job schedules for each machine in the grid by taking static restrictions and dynamic parameters of jobs and machines
into consideration. The main purpose of this paper is to develop an efficient job scheduling algorithm to maximize the resource utilization and minimize processing time of the jobs. Queues can be optimized by using various scheduling algorithms depending upon the performance criteria to be improved e.g. response
time, throughput. The work has been done in MATLAB using the parallel computing toolbox.
GROUPING BASED JOB SCHEDULING ALGORITHM USING PRIORITY QUEUE AND HYBRID ALGOR...ijgca
Grid computing enlarge with computing platform which is collection of heterogeneous computing resources connected by a network across dynamic and geographically dispersed organization to form a distributed high performance computing infrastructure. Grid computing solves the complex computing problems amongst multiple machines. Grid computing solves the large scale computational demands in a high performance computing environment. The main emphasis in the grid computing is given to the resource management and the job scheduler .The goal of the job scheduler is to maximize the resource utilization and minimize the processing time of the jobs. Existing approaches of Grid scheduling doesn’t give much emphasis on the performance of a Grid scheduler in processing time parameter. Schedulers allocate resources to the jobs to be executed using the First come First serve algorithm. In this paper, we have provided an optimize algorithm to queue of the scheduler using various scheduling methods like Shortest Job First, First in First out, Round robin. The job scheduling system is responsible to select best suitable machines in a grid for user jobs. The management and scheduling system generates job schedules for each machine in the grid by taking static restrictions and dynamic parameters of jobs and machines into consideration. The main purpose of this paper is to develop an efficient job scheduling algorithm to maximize the resource utilization and minimize processing time of the jobs. Queues can be optimized by using various scheduling algorithms depending upon the performance criteria to be improved e.g. response time, throughput. The work has been done in MATLAB using the parallel computing toolbox.
Sharing of cluster resources among multiple Workflow Applicationsijcsit
Many computational solutions can be expressed as workflows. A Cluster of processors is a shared
resource among several users and hence the need for a scheduler which deals with multi-user jobs
presented as workflows. The scheduler must find the number of processors to be allotted for each workflow
and schedule tasks on allotted processors. In this work, a new method to find optimal and maximum
number of processors that can be allotted for a workflow is proposed. Regression analysis is used to find
the best possible way to share available processors, among suitable number of submitted workflows. An
instance of a scheduler is created for each workflow, which schedules tasks on the allotted processors.
Towards this end, a new framework to receive online submission of workflows, to allot processors to each
workflow and schedule tasks, is proposed and experimented using a discrete-event based simulator. This
space-sharing of processors among multiple workflows shows better performance than the other methods
found in literature. Because of space-sharing, an instance of a scheduler must be used for each workflow
within the allotted processors. Since the number of processors for each workflow is known only during
runtime, a static schedule can not be used. Hence a hybrid scheduler which tries to combine the advantages
of static and dynamic scheduler is proposed. Thus the proposed framework is a promising solution to
multiple workflows scheduling on cluster.
Reinforcement learning based multi core scheduling (RLBMCS) for real time sys...IJECEIAES
Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
Dynamic Task Scheduling based on Burst Time Requirement for Cloud EnvironmentIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
The Cloud computing becomes an important topic
in the area of high performance distributed computing. On the
other hand, task scheduling is considered one the most significant
issues in the Cloud computing where the user has to pay for the
using resource based on the time. Therefore, distributing the
cloud resource among the users' applications should maximize
resource utilization and minimize task execution Time. The goal
of task scheduling is to assign tasks to appropriate resources that
optimize one or more performance parameters (i.e., completion
time, cost, resource utilization, etc.). In addition, the scheduling
belongs to a category of a problem known as an NP-complete
problem. Therefore, the heuristic algorithm could be applied to
solve this problem. In this paper, an enhanced dependent task
scheduling algorithm based on Genetic Algorithm (DTGA) has
been introduced for mapping and executing an application’s
tasks. The aim of this proposed algorithm is to minimize the
completion time. The performance of this proposed algorithm has
been evaluated using WorkflowSim toolkit and Standard Task
Graph Set (STG) benchmark.
RSDC (Reliable Scheduling Distributed in Cloud Computing)IJCSEA Journal
In this paper we will present a reliable scheduling algorithm in cloud computing environment. In this algorithm we create a new algorithm by means of a new technique and with classification and considering request and acknowledge time of jobs in a qualification function. By evaluating the previous algorithms, we understand that the scheduling jobs have been performed by parameters that are associated with a failure rate. Therefore in the roposed algorithm, in addition to previous parameters, some other important parameters are used so we can gain the jobs with different scheduling based on these parameters. This work is associated with a mechanism. The major job is divided to sub jobs. In order to balance the jobs we should calculate the request and acknowledge time separately. Then we create the scheduling of each job by calculating the request and acknowledge time in the form of a shared job. Finally efficiency of the system is increased. So the real time of this algorithm will be improved in comparison with the other algorithms. Finally by the mechanism presented, the total time of processing in cloud computing is improved in comparison with the other algorithms.
Job-shop manufacturing environment requires planning of schedules for the systems of low-volume having numerous variations. For a job-shop scheduling, ‘k’ number of operations and ‘n’ number of jobs on ‘m’ number of machines processed through an assured objective function to be minimized (makespan). This paper presents a capable genetic algorithm for the job-shop scheduling problems among operating parameters such as random population generation with a population size of 50, operation based chromosome structure, tournament selection as selection scheme, 2-point random crossover with probability 80%, 2-point mutation with probability 20%, elitism, repairing of chromosomes and no. of iteration is 1000. An algorithm is programmed for job shop scheduling problem using MATLAB 2009 a 7.8. The proposed genetic algorithm with certain operating parameters is applied to the two case studies taken from literature. The results also show that genetic algorithm is the best optimization technique for solving the scheduling problems of job shop manufacturing systems evolving shortest processing time and transportation time due to its implications to more practical and integrated problems.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A novel methodology for task distributionijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
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• Copatiable with IDM8000 CCR
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• Compatible with commercial and Defence aviation CCR system.
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• Compatible with MAFI CCR system.
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HYBRID GENETIC ALGORITHM FOR BI-CRITERIA MULTIPROCESSOR TASK SCHEDULING WITH COMMUNICATION DELAY
1. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.4, October 2016
DOI:10.5121/acii.2016.3403 19
HYBRID GENETIC ALGORITHM FOR BI-CRITERIA
MULTIPROCESSOR TASK SCHEDULING WITH
COMMUNICATION DELAY
Sunita Dhingra1
*, Ashwani K. Dhingra2
,SatinderBal Gupta3
and Ranjit Biswas4
1
Department of Computer science & Engineering, 2
Department of Mechanical
Engineering,
University Institute of Engineering & Technology,
MaharshiDayanand University Rohtak-124001 Haryana, India
3
Department of Computer Science, Vaish College of Engineering Rohtak-124001
Haryana, India
4
Department of Computer Science & Engineering, JamiaHamdard University New
Delhi-110062 , India
ABSTRACT
Present work considers the minimization of the bi-criteria function including weighted sum of makespan
and total completion time for a Multiprocessor task scheduling problem.Genetic algorithm is the most
appealing choice for the different NP hard problems including multiprocessor task scheduling.
Performance of genetic algorithm depends on the quality of initial solution as good initial solution provides
the better results. Different list scheduling heuristics based hybrid genetic algorithms (HGAs) have been
proposed and developedfor the problem. Computational analysis with the help of defined performance
index has been conducted on the standard task scheduling problems for evaluating the performance of the
proposed HGAs. The analysis shows that the ETF-GA is quite efficient and best among the other heuristic
based hybrid genetic algorithms in terms of solution quality especially for large and complex problems.
Keywords:
Multiprocessor task scheduling, heuristics, Hybrid Genetic algorithm, makespan, Total Completion time.
1.INTRODUCTION
Parallel processing is the most promising approach for meeting the increased computational
requirements thathas introduced a number of problems including multiprocessor task scheduling.
Usually a large program is divided into smaller tasks havingsome dependencies representing the
precedence constraints such that a task cannot be started until all its predecessors have finished.
The goal of a task scheduling algorithm is to schedule all the tasks on the given number of
available processors without violating precedence constraints so as to minimize the different
performance measures in order to maximize the throughput and utilization of the system. The
multiprocessor task scheduling problem with precedence constraints belongs to the class of NP
hard problems and its importance led to several comparative studies. The conventional research
on scheduling considers heuristic based algorithms which are divided into three categories: list
scheduling, clustering based and duplication based. Among heuristic algorithms, List scheduling
algorithms are commonly used and work by assigning priority to each task according to attributes
such as t-level (top level), b-level (bottom level), static level (sl) and ALAP (As Late As Possible)
2. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.4, October 2016
20
start time[1-5,10].The task with highest priority is assigned to the processor which gives the
earliest start time. Task selection can be random or based on some rules for the task having the
same priority. The heuristic algorithms follow the procedure that narrow the search to a very
small portion of the solution space. Heuristic-based approaches have greedy nature due to which
they are are not likely to produce reliable results for a wide rangeof problems, particularly when
the density of the scheduling problem increases.
Therefore, researchers go for metaheuristics such as GeneticAlgorithm,Simulated annealing,
Tabusearch, Particle swarm optimization etc.for the sake of better quality schedules.Most of the
metaheuristicsespecially Genetic Algorithm(GA) outperformed traditional heuristic based
scheduling algorithms at the cost of extra time andcomputing effort[6].It is therefore
hybridization of metaheuristicswith heuristics is the next choice for improving the solution
quality.
In the present work, an attempt has been made for hybridization of different list scheduling
heuristics with genetic algorithm for the multiprocessor task scheduling problem with precedence
constraints on homogeneous processors for minimizing the considered bi-criteria objective
function. The remaining part of the paper is organized as follows: section 2 is based on literature
review in the fieldof hybrid algorithms related to task scheduling. Problem formulation along
with assumptions is presented in section 3. Section 4 describes the different hybrid Genetic
Algorithm (HGAs). Section 5 gives analysis of experimental results with discussions followed by
the conclusionin section 6.
2. RELATED WORK
As the complexity of the scheduling problem increases the heuristic algorithms fails to provide
the reliable results.Metaheuristic based scheduling algorithms obtain schedules of better quality
but at the expense of more computing efforts due to limited exploration ability.Houet al.
[6]proposed the first and most important work that has used GA for multiprocessor tasks
scheduling which uses theheight of tasks in input DAG.Though the algorithm is very simple in
terms of computational complexity, but it cannot guarantee that the search space is global due to
which someviable schedules are not accessible [11]. Houetal.[6] andDhodhi& Ahmad [7]
developed a new technique in combination with the ISH [2] and DSH [2] based list scheduling
heuristicswith GAs and called as problem-space genetic algorithms(PSGAs).The analysis of
results showed that the GA when combined with heuristics could work efficiently and schedule
the taskson several processors. In literature, different methods have also beenattempted to
combine the heuristic and genetic approaches for the solution of theproblem [7, 8 23].
Sivanandam et al. [13]proposed a hybrid algorithm in support of particle swarm
optimization/simulated annealing (PSO/SA) for static allocation of tasks in a heterogeneous
distributed computing system with the objective of minimizing the cost. Implementation has been
carried out on different PSO algorithm with Simulated Annealing. Different experiments have
been performed on the benchmark problems and shows that the proposed hybrid method was
effective and efficient in finding near optimal solutions.Yooet al. [14] proposed a multi-objective
hybrid genetic algorithm (MOHGA) for real-time tasks on heterogeneous multiprocessor
environment with the purpose of minimizing the total tardiness and completion time
simultaneously. The adaptive weight approach has been used for multiple objectives. The
convergence of GA is enhanced by introducing the probability of SA as the measure for taking
3. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.4, October 2016
21
new trial solution. The effectiveness of proposed algorithm has been checked by various
experiments andit was concluded that the MOHGAprovides improved resultsas compared to
other algorithms without communication cost.Dahal et al. [15]proposed a hybrid algorithm for
dynamic scheduling. Thewell known heuristics such as ‘Earliest Deadline First (EDF)’ and
‘Shortest Computation Time First (SCTF)’ has been hybrid with the Genetic Algorithm (GA). It
was concluded that hybridization of GA with SCTF provides better performance as compared to
the EDF based hybrid GA.Azghadi et al. [16] developed an immune genetic approach for
multiprocessor task scheduling problem and proves to be more effective.Jouglet et al. [17]
proposed a memetic algorithm (MA) for the hybrid flow shop scheduling with multiprocessor
tasks. They hybridized theGenetic Algorithm (GA) and Constraint Programming (CP) for a
Memetic Algorithm (MA) and concluded the superiority of proposed MA.Hwang et al. [18]
proposed priority-based GA which uses a new encoding mechanism with a multi-functional
chromosome that uses the priority representation— so-called priority-based multi-chromosome
(PMC). They addressed the problem of multiprocessor task scheduling with communication cost.
From results, it is concluded that proposed priority-based GA has effective performance in
various parallel environments for scheduling methods.
Kim et al. [19]considered heuristic method for a deterministic scheduling problem where multiple
jobs with s-precedence relations are processed on multiple identical parallel machines for
minimization of the total completion time.Goh et al. [21]considered a heterogeneous
multiprocessor scheduling problem with precedence constraint and proposed a hybrid
evolutionary algorithm (HEA) formakespan minimization. The method considered the partial list
scheduling and duplication scheduling heuristic for exploiting the intrinsic structure of the
solution and specialized genetic operators for promoting the exploration of the search space.
Experiments were carried on a set of benchmark problems and it was concluded that the
proposed HEAprovide better results. Wen et al. [22] incorporated GA with both Variable
Neighborhood Search (VNS) and a heuristic extracted from traditional list scheduling algorithms
for the minimization of makespan in the heterogeneous multiprocessor scheduling problem
resulting into a heuristic based hybrid genetic variable neighborhood search algorithm. The
performance of proposed approach was compared with four related algorithms, HEFT, AIS, VNS
and IGA on standard benchmarks problems and it was concluded that proposed algorithm
constantly outperforms the other four algorithms in terms of schedule quality.
Mohamed et al.[23]proposed the Modified List Scheduling Heuristic (MLSH) along with the
hybridization with theGenetic Algorithm for multiprocessor task scheduling system and
concluded the superiority when compared to others.Roy et al. [24]considered task scheduling in
multiprocessor systems and proposed a heuristic based Genetic Algorithm by choosing the
eligible processor on educated guess. Variation of HLFET algorithm with genetic algorithm was
proposed and experiments were performed on Standard Task Graphs (STG).It was concluded that
the algorithm has better average makespan than Elitism stepping method in lesser number of
evaluations.
Hybrid methods of scheduling have beenmotivating by the fact that each type of scheduling
technique has its own supremacy and restriction. Therefore, the present work considers the
development of different Hybrid Genetic Algorithms (HGAs) with commonly used list heuristics
for improving the quality of solution in the presence of communication cost.
4. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.4, October 2016
22
3. PROBLEM STATEMENT
The considered work includes the minimizing the weighted sum of makespan and total
completion time for multiprocessor task scheduling problem. Various assumptions and the fitness
function considered are illustrated below:
3.1 ASSUMPTIONS
• The problem is deterministic with known values of data communication time, task
dependencies & execution time.
• A DAG is used to represent the dependencies along with execution time and
communication cost.
• Communication cost is considered only when the tasks are scheduled on different
processor otherwise it is taken as zero.
• All the processors are homogeneous.i.esame execution time for all the processors.
• Pre-emption of tasks is not allowed.
• Task duplication is not allowed.
• All processors & tasks are accessible at time t = 0.
3.2FITNESSFUNCTION
Fitness function considered in the present work deals with minimizing weighted sum of
makespan and total completion time. Makespan of a schedule is the time at which its last task
completes. Total completion time of a schedule is the summation of completion times of all the
tasks of that schedule. For the requirement of maximum utilization of resources, increase in
throughput, load balancing etc, authors have proposed the bi-criteria decision making fitness
function including the two performance measure for multiprocessor task scheduling which has
been framed as:
Where, F is the bi-criteria fitness function, Cmax is the makespan and Ci is the completion time of
ith task of a schedule with ‘α’ the weight coefficient as per the priority of the performance
measures to be minimised.
4. PROPOSED HYBRID GENETIC ALGORITHM (HGA)
It has been generallyestablished that finding optimality to NP hard problems is not a feasible
option since large amount of computational time is needed for finding of such solutions. In
reality, a superior initial solution can be obtained by a heuristic in arational computational time. A
Genetic algorithm is a valuable population based approach for the multiprocessor task scheduling.
Consequently many researchers [6, 12, 19] have reported success withgenetic algorithm in
achieving good solutions to combinatorial optimization problems. A genetic algorithm starts with
an initial population which can be generated arbitrarily or based on some rules, heuristics and
algorithms. Then in each generation the population follows the sequence of encoding, fitness
evaluation, selection, crossover and mutation until some stopping criteria is met.
−+= ∑=
n
i
iCCMinF
1
max )1( αα
5. Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.4, October 2016
23
Performance of GA depends on the superior chromosomes in initial population for faster
convergence [24]. So, in the present work different list scheduling heuristics (table 1) is used to
obtain a seed sequence which is then combined with (ps-1) randomly generated sequences to give
initial population of size ps. The proposed hybrid genetic algorithm is described below:
Step 1 Encoding
a) Encoding give the illustration of a chromosome. In the present work, chromosome is taken as
(T, P) pair where T is task sequence t1, t2,.......,tn& P is owed processor sequence p1,p2,....,pn.
b) Each task sequence is a variation of task numbers & each processor sequence is a variation of
processor numbers (1, 2... m) with length equal to number of tasks.
Step 2 Initialization
Each task sequence is a variation of task numbers, so each task will be processed according to its
emergence. Each task in the sequence should come out before all of its children and parents due
to dependency. Some mechanism is needed for validating the invalid sequences. The steps for
generation of initial population:
1. Use scheduling heuristic (table 1) for generating the seed sequence.
2. Generate (ps – 1) population arbitrarilyusing the following steps
a) Generate the suitable task sequences (TS) of (Ps-1) using the algorithm as stated
by Bonyadi and Moghaddam[20].
b) Generate the processor sequences (PS) of ps-1arbitrarily.
c) Each task sequence (T) is mapped from TS forarbitrarily selecting the processor
sequence (P) giving each chromosome in the form (T, P) pairsuch that the task
sequence should be followed by mapped processor sequence.
3. Merge the seed sequence with randomly generated population as per population size (Ps).
Step 3 Reproduction
As the task and processor sequences have different nature, so, different reproduction operators are
used for both sequences. Both the sequences are firstly separated from a chromosome and then
used independently for performing crossover and mutation. Task sequences after reproduction
may not follow dependency, so a mechanismstated by Bonyadi and Moghaddam [20] has
beenapplied for validating the task sequences. The valid task sequences after reproduction (TS’)
have mapped to the processor sequences based on minimum fitness value.
The new offspring’s are generated with the following steps:
a) Scores every member of the present population by computing the fitness function
included weighted sum of makespan and total completion time.
b) The individuals as per fitness in the current population act as elite and admit in the next
population.
c) Selects parents for reproduction based on the fitness value as per the selection function.
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Step 4: Stopping criteria
The algorithm stops when maximum number of generations reaches 100.
4.1 List Scheduling heuristics
List scheduling heuristicsare based on assigning priorities to tasks and then allocating the highest
priority task to processor which gives the minimum start time. The present work uses five list
scheduling heuristics i.e. HLFET,ISH,MCP,ETF,DLS to input one best sequence in initial
population of GA resulting into five different hybrid genetic algorithms HLFET-GA, ISH-GA,
MCP-GA,ETF-GA and DLS-GA. Different heuristics used for the initial seed sequence in the
different hybridgenetic algorithmic are shown in table 1.
Table 1. Different Heuristics for initial feasible solution of different Hybrid GAs
5. RESULTS AND DISCUSSION
The proposed different Hybrid Genetic algorithms (HGA’s) have been implemented in MATLAB
environment. First, a sample multiprocessor task scheduling problem (T18) has beenconsidered
to set the parameters of the proposed GA for the bi-criteria multiprocessor task scheduling
problem. The different parameters setting of GA have been optimised for a sample problem using
design of experiments. Parameters fixed for different proposed and developed HGAs are shown
in table 2.
The value of total completion time is very large as compared to makespan and values of weight
coefficient (α) must be normalized in such a way that minimization of fitness values have
negligible impact on the makespan and total completion time for equal priority. The normalized
values eliminate the effects of certain gross influences.
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25
Table 2. Parameters fixed for different HGAs.
There are different methods of normalization and every method requires the best value of the
objectives.Therefore, normalization has been done with the help of different experiments from the
heuristics on the different problems and a value of weight coefficient (α) for a particular problem
are calculated from the relation (irrespective of number of processors) and is given below:
Weight Coefficient (α) =
+
−
Bestbest
Best
MakespanetiontimeTotalCompl
Makespan
1
The comparative analysis has been done by computing the performance Index (PI) as:-
Performance Index (PI) (%) = 100
lg
1 ×
−
−
solution
solutionsolution
Best
BestorithmA
Algorithmsolution is the average solution obtained by the different algorithmsand Bestsolution is the
best solution obtained from different algorithmin all the runs. Performance Indexcloser to 100%
provides the good results. Some of the standard problems [12]along with their computed weight
coefficient (α) have been used for the comparative analysis among proposed HGA’s as shown in
table 3.
Table 3.Standard Multiprocessor Task Scheduling Problems along with weight coefficient
All the Hybrid GAs have been run five times for taking final average and comparative analysis
for makespan and total completion time criteria has been shown in fig. 1 and 2 respectively. The
problem T9 and T18 having the variable communication cost and the problems T14_1, T14_2,
T16_1 & T16_2 have the fixed communication cost. It can also be seen that the communication
cost has great impact onthe quality of solution. For the variable communication cost, ISH-GA and
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26
ETF-GA provides the best results for the problem T9 and as the task size increase i.e. T18, the
ETF-GA provides the superior results as comparison to others with all the processors for the
makespan and total completion time.
For the problem having fixed communication cost, all the HGA’s are comparable to each others
for all the processors having lesser communication cost. As the communication cost increases
along with task size, all the HGA’s (Except HLFET-GA) provides the similar results. Therefore,
in order to compare the different HGAs, Average Performance Index for all the problems has
been calculated for 2, 3 and 4 processors and shown in table 4.
Average performance index shows the ETF-GA algorithm provides better results for all the
problems considered with average PI of 95.55% and 95.57% for 2 processors, 98.58% and
98.09% for 3 processors and 99.47% and 98.44% for 4 processors for minimization of makespan
and total completion time respectively. Therefore, ETF based HGA when compared to others
provides the best compromise results for the makespan and total completion time criteria for the
larger size multiprocessor task scheduling problems with 2, 3 and 4 processors.
Figure 1. Performance Index (%) of different Hybrid GA for makespan (a) Two Processors (b) Three
Processors (c) Four Processors
.
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Figure 2. Performance Index (%) of different Hybrid GA for Total Completion Time (a) Two Processors
(b) Three Processors (c) Four Processors
Table 4. Average Performance Index (%) for different HGAs on different processors
MS: Makespan& TCT: Total Completion Time
6. CONCLUSIONS
Present work considers the bi-criteria multiprocessor task scheduling problem on homogeneous
processors with objective of minimizing the weighted sum of makespan and total completion
time. Initial solution from the well known list heuristics have been obtained and combined with
initial population of genetic algorithm to form five different Hybrid Genetic Algorithms (HGA’s)
for the problem. The computation analysis has been done on the some standard benchmark
multiprocessor task scheduling problem with variable and fixed communication cost.
Performance analysis is done with the help of defined performance indexand reveals that ETF
based hybrid GA (ETF-GA) provides finer results for the makespan and total completion time
criteriaparticularlyfor larger and complex problems.
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